{"product_id":"knowledge-based-bioinformatics-from-analysis-to-interpretation-hardback-9780470748312","title":"Knowledge-Based Bioinformatics; From Analysis to Interpretation (Hardback) 9780470748312","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eKnowledge-Based Bioinformatics\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFrom Analysis to Interpretation\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGil Alterovitz (Edited by), G Alterovitz (Author), Marco Ramoni (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470748312, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 16 July 2010\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e400 pages\u003cbr\u003e23.6 x 16 x 2.7 cm, 0.699 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThere is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine.  \u003cp\u003eKey Features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology.\u003c\/li\u003e \u003cli\u003eHelps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions.\u003c\/li\u003e \u003cli\u003eProvides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics.\u003c\/li\u003e \u003cli\u003eWritten by leading international experts in this field.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eStudents, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eList of Contributors.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I FUNDAMENTALS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 1 Knowledge-Driven Approaches.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Knowledge-based bioinformatics\u003c\/b\u003e (\u003ci\u003eEric Karl Neumann\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 Formal reasoning for bioinformatics.\u003c\/p\u003e \u003cp\u003e1.3 Knowledge representations.\u003c\/p\u003e \u003cp\u003e1.4 Collecting explicit knowledge.\u003c\/p\u003e \u003cp\u003e1.5 Representing common knowledge.\u003c\/p\u003e \u003cp\u003e1.6 Capturing novel knowledge.\u003c\/p\u003e \u003cp\u003e1.7 Knowledge discovery applications.\u003c\/p\u003e \u003cp\u003e1.8 Semantic harmonization: the power and limitation of ontologies.\u003c\/p\u003e \u003cp\u003e1.9 Text mining and extraction.\u003c\/p\u003e \u003cp\u003e1.10 Gene expression.\u003c\/p\u003e \u003cp\u003e1.11 Pathways and mechanistic knowledge.\u003c\/p\u003e \u003cp\u003e1.12 Genotypes and phenotypes.\u003c\/p\u003e \u003cp\u003e1.13 The Web's role in knowledge mining.\u003c\/p\u003e \u003cp\u003e1.14 New frontiers.\u003c\/p\u003e \u003cp\u003e1.15 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Knowledge-driven approaches to genome-scale analysis\u003c\/b\u003e (\u003ci\u003eHannah Tipney and Lawrence Hunter\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e2.1 Fundamentals.\u003c\/p\u003e \u003cp\u003e2.2 Challenges in knowledge-driven approaches.\u003c\/p\u003e \u003cp\u003e2.3 Current knowledge-based bioinformatics tools.\u003c\/p\u003e \u003cp\u003e2.4 3R systems: reading, reasoning and reporting the way towards biomedical discovery.\u003c\/p\u003e \u003cp\u003e2.5 The Hanalyzer: a proof of 3R concept.\u003c\/p\u003e \u003cp\u003e2.6 Acknowledgements.\u003c\/p\u003e \u003cp\u003e2.7 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Technologies and best practices for building bio-ontologies\u003c\/b\u003e (\u003ci\u003eMikel Egaña Aranguren, Robert Stevens, Erick Antezana, Jesualdo Tomás Fernández-Breis, Martin Kuiper, and Vladimir Mironov\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Knowledge representation languages and tools for building bio-ontologies.\u003c\/p\u003e \u003cp\u003e3.3 Best practices for building bio-ontologies.\u003c\/p\u003e \u003cp\u003e3.4 Conclusion.\u003c\/p\u003e \u003cp\u003e3.5 Acknowledgements.\u003c\/p\u003e \u003cp\u003e3.6 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Design, implementation and updating of knowledge bases\u003c\/b\u003e (\u003ci\u003eSarah Hunter, Rolf Apweiler, and Maria Jesus Martin\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Sources of data in bioinformatics knowledge bases.\u003c\/p\u003e \u003cp\u003e4.3 Design of knowledge bases.\u003c\/p\u003e \u003cp\u003e4.4 Implementation of knowledge bases.\u003c\/p\u003e \u003cp\u003e4.5 Updating of knowledge bases.\u003c\/p\u003e \u003cp\u003e4.6 Conclusions.\u003c\/p\u003e \u003cp\u003e4.7 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 2 Data-Analysis Approaches.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Classical statistical learning in bioinformatics\u003c\/b\u003e (\u003ci\u003eMark Reimers\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Significance testing.\u003c\/p\u003e \u003cp\u003e5.3 Exploratory analysis.\u003c\/p\u003e \u003cp\u003e5.4 Classification and prediction.\u003c\/p\u003e \u003cp\u003e5.5 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Bayesian methods in genomics and proteomics studies\u003c\/b\u003e (\u003ci\u003eNing Sun and Hongyu Zhao\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Bayes theorem and some simple applications.\u003c\/p\u003e \u003cp\u003e6.3 Inference of population structure from genetic marker data.\u003c\/p\u003e \u003cp\u003e6.4 Inference of protein binding motifs from sequence data.\u003c\/p\u003e \u003cp\u003e6.5 Inference of transcriptional regulatory networks from joint analysis of protein–DNA binding data and gene expression data.\u003c\/p\u003e \u003cp\u003e6.6 Inference of protein and domain interactions from yeast two-hybrid data.\u003c\/p\u003e \u003cp\u003e6.7 Conclusions.\u003c\/p\u003e \u003cp\u003e6.8 Acknowledgements.\u003c\/p\u003e \u003cp\u003e6.9 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Automatic text analysis for bioinformatics knowledge discovery\u003c\/b\u003e (\u003ci\u003eDietrich Rebholz-Schuhmann and Jung-jae Kim\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Information needs for biomedical text mining.\u003c\/p\u003e \u003cp\u003e7.3 Principles of text mining.\u003c\/p\u003e \u003cp\u003e7.4 Development issues.\u003c\/p\u003e \u003cp\u003e7.5 Success stories.\u003c\/p\u003e \u003cp\u003e7.6 Conclusion.\u003c\/p\u003e \u003cp\u003e7.7 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II APPLICATIONS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 3 Gene and Protein Information.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fundamentals of gene ontology functional annotation\u003c\/b\u003e (\u003ci\u003eVarsha K. Khodiyar, Emily C. Dimmer, Rachael P. Huntley, and Ruth C. Lovering\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Gene Ontology (GO).\u003c\/p\u003e \u003cp\u003e8.3 Comparative genomics and electronic protein annotation.\u003c\/p\u003e \u003cp\u003e8.4 Community annotation.\u003c\/p\u003e \u003cp\u003e8.5 Limitations.\u003c\/p\u003e \u003cp\u003e8.6 Accessing GO annotations.\u003c\/p\u003e \u003cp\u003e8.7 Conclusions.\u003c\/p\u003e \u003cp\u003e8.8 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Methods for improving genome annotation\u003c\/b\u003e (\u003ci\u003eJonathan Mudge and Jennifer Harrow\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e9.1 The basis of gene annotation.\u003c\/p\u003e \u003cp\u003e9.2 The impact of next generation sequencing on genome annotation.\u003c\/p\u003e \u003cp\u003e9.3 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Sequences from prokaryotic, eukaryotic, and viral genomes available clustered according to phylotype on a Self-Organizing Map\u003c\/b\u003e (\u003ci\u003eTakashi Abe, Shigehiko Kanaya, and Toshimichi Ikemura\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Batch-learning SOM (BLSOM) adapted for genome informatics.\u003c\/p\u003e \u003cp\u003e10.3 Genome sequence analyses using BLSOM.\u003c\/p\u003e \u003cp\u003e10.4 Conclusions and discussion.\u003c\/p\u003e \u003cp\u003e10.5 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 4 Biomolecular Relationships and Meta-Relationships.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Molecular network analysis and applications\u003c\/b\u003e (\u003ci\u003eMinlu Zhang, Jingyuan Deng, Chunsheng V. Fang, Xiao Zhang, and Long Jason Lu\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Topology analysis and applications.\u003c\/p\u003e \u003cp\u003e11.3 Network motif analysis.\u003c\/p\u003e \u003cp\u003e11.4 Network modular analysis and applications.\u003c\/p\u003e \u003cp\u003e11.5 Network comparison.\u003c\/p\u003e \u003cp\u003e11.6 Network analysis software and tools.\u003c\/p\u003e \u003cp\u003e11.7 Summary.\u003c\/p\u003e \u003cp\u003e11.8 Acknowledgement.\u003c\/p\u003e \u003cp\u003e11.9 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Biological pathway analysis: an overview of Reactome and other integrative pathway knowledge bases\u003c\/b\u003e (\u003ci\u003eRobin A. Haw, Marc E. Gillespie, and Michael A. Caudy\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e12.1 Biological pathway analysis and pathway knowledge bases.\u003c\/p\u003e \u003cp\u003e12.2 Overview of high-throughput data capture technologies and data repositories.\u003c\/p\u003e \u003cp\u003e12.3 Brief review of selected pathway knowledge bases.\u003c\/p\u003e \u003cp\u003e12.4 How does information get into pathway knowledge bases?\u003c\/p\u003e \u003cp\u003e12.5 Introduction to data exchange languages.\u003c\/p\u003e \u003cp\u003e12.6 Visualization tools.\u003c\/p\u003e \u003cp\u003e12.7 Use case: pathway analysis in Reactome using statistical analysis of high-throughput data sets.\u003c\/p\u003e \u003cp\u003e12.8 Discussion: challenges and future directions of pathway knowledge bases.\u003c\/p\u003e \u003cp\u003e12.9 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Methods and challenges of identifying biomolecular relationships and networks associated with complex diseases\/phenotypes, and their application to drug treatments\u003c\/b\u003e (\u003ci\u003eMie Rizig\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e13.1 Complex traits: clinical phenomenology and molecular background.\u003c\/p\u003e \u003cp\u003e13.2 Why it is challenging to infer relationships between genes and phenotypes in complex traits? 317\u003c\/p\u003e \u003cp\u003e13.3 Bottom-up or top-down: which approach is more useful in delineating complex traits key drivers?\u003c\/p\u003e \u003cp\u003e13.4 High-throughput technologies and their applications in complex traits genetics.\u003c\/p\u003e \u003cp\u003e13.5 Integrative systems biology: a comprehensive approach to mining high-throughput data.\u003c\/p\u003e \u003cp\u003e13.6 Methods applying systems biology approach in the identification of functional relationships from gene expression data.\u003c\/p\u003e \u003cp\u003e13.7 Advantages of networks exploration in molecular biology and drug discovery.\u003c\/p\u003e \u003cp\u003e13.8 Practical examples of applying systems biology approaches and network exploration in the identification of functional modules and disease-causing genes in complex phenotypes\/diseases.\u003c\/p\u003e \u003cp\u003e13.9 Challenges and future directions.\u003c\/p\u003e \u003cp\u003e13.10 References.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTrends and conclusion.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52278026764568,"sku":"9780470748312","price":61.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470748312.jpg?v=1781456414","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/knowledge-based-bioinformatics-from-analysis-to-interpretation-hardback-9780470748312","provider":"Freshly Printed Books","version":"1.0","type":"link"}